Overview

Dataset statistics

Number of variables17
Number of observations1570
Missing cells4197
Missing cells (%)15.7%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory208.6 KiB
Average record size in memory136.1 B

Variable types

DateTime1
Categorical6
Text2
Numeric8

Alerts

app_id has constant value "com.super.app"Constant
app_type has constant value "android"Constant
app_name has constant value "Super App 2000"Constant
event_name has constant value "super_event"Constant
campaign_id is highly imbalanced (71.1%)Imbalance
campaign_name is highly imbalanced (71.1%)Imbalance
ad_id has 117 (7.5%) missing valuesMissing
ad_name has 117 (7.5%) missing valuesMissing
impressions has 81 (5.2%) missing valuesMissing
clicks has 81 (5.2%) missing valuesMissing
installs has 744 (47.4%) missing valuesMissing
spend has 81 (5.2%) missing valuesMissing
events_d0 has 744 (47.4%) missing valuesMissing
events_d7 has 744 (47.4%) missing valuesMissing
unique_events_d0 has 744 (47.4%) missing valuesMissing
unique_events_d7 has 744 (47.4%) missing valuesMissing
clicks is highly skewed (γ1 = 22.2504011)Skewed
impressions has 165 (10.5%) zerosZeros
clicks has 255 (16.2%) zerosZeros
spend has 140 (8.9%) zerosZeros
events_d0 has 511 (32.5%) zerosZeros
events_d7 has 494 (31.5%) zerosZeros
unique_events_d0 has 511 (32.5%) zerosZeros
unique_events_d7 has 494 (31.5%) zerosZeros

Reproduction

Analysis started2024-07-01 05:37:19.128906
Analysis finished2024-07-01 05:37:41.990091
Duration22.86 seconds
Software versionydata-profiling v4.8.3
Download configurationconfig.json

Variables

Distinct61
Distinct (%)3.9%
Missing0
Missing (%)0.0%
Memory size12.4 KiB
Minimum2022-06-01 00:00:00
Maximum2022-07-31 00:00:00
2024-07-01T02:37:42.318120image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-07-01T02:37:42.851161image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)

app_id
Categorical

CONSTANT 

Distinct1
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size12.4 KiB
com.super.app
1570 

Length

Max length13
Median length13
Mean length13
Min length13

Characters and Unicode

Total characters20410
Distinct characters10
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowcom.super.app
2nd rowcom.super.app
3rd rowcom.super.app
4th rowcom.super.app
5th rowcom.super.app

Common Values

ValueCountFrequency (%)
com.super.app 1570
100.0%

Length

2024-07-01T02:37:43.252540image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-07-01T02:37:43.596987image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
ValueCountFrequency (%)
com.super.app 1570
100.0%

Most occurring characters

ValueCountFrequency (%)
p 4710
23.1%
. 3140
15.4%
c 1570
 
7.7%
o 1570
 
7.7%
m 1570
 
7.7%
s 1570
 
7.7%
u 1570
 
7.7%
e 1570
 
7.7%
r 1570
 
7.7%
a 1570
 
7.7%

Most occurring categories

ValueCountFrequency (%)
(unknown) 20410
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
p 4710
23.1%
. 3140
15.4%
c 1570
 
7.7%
o 1570
 
7.7%
m 1570
 
7.7%
s 1570
 
7.7%
u 1570
 
7.7%
e 1570
 
7.7%
r 1570
 
7.7%
a 1570
 
7.7%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 20410
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
p 4710
23.1%
. 3140
15.4%
c 1570
 
7.7%
o 1570
 
7.7%
m 1570
 
7.7%
s 1570
 
7.7%
u 1570
 
7.7%
e 1570
 
7.7%
r 1570
 
7.7%
a 1570
 
7.7%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 20410
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
p 4710
23.1%
. 3140
15.4%
c 1570
 
7.7%
o 1570
 
7.7%
m 1570
 
7.7%
s 1570
 
7.7%
u 1570
 
7.7%
e 1570
 
7.7%
r 1570
 
7.7%
a 1570
 
7.7%

app_type
Categorical

CONSTANT 

Distinct1
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size12.4 KiB
android
1570 

Length

Max length7
Median length7
Mean length7
Min length7

Characters and Unicode

Total characters10990
Distinct characters6
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowandroid
2nd rowandroid
3rd rowandroid
4th rowandroid
5th rowandroid

Common Values

ValueCountFrequency (%)
android 1570
100.0%

Length

2024-07-01T02:37:43.908714image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-07-01T02:37:44.072199image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
ValueCountFrequency (%)
android 1570
100.0%

Most occurring characters

ValueCountFrequency (%)
d 3140
28.6%
a 1570
14.3%
n 1570
14.3%
r 1570
14.3%
o 1570
14.3%
i 1570
14.3%

Most occurring categories

ValueCountFrequency (%)
(unknown) 10990
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
d 3140
28.6%
a 1570
14.3%
n 1570
14.3%
r 1570
14.3%
o 1570
14.3%
i 1570
14.3%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 10990
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
d 3140
28.6%
a 1570
14.3%
n 1570
14.3%
r 1570
14.3%
o 1570
14.3%
i 1570
14.3%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 10990
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
d 3140
28.6%
a 1570
14.3%
n 1570
14.3%
r 1570
14.3%
o 1570
14.3%
i 1570
14.3%

app_name
Categorical

CONSTANT 

Distinct1
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size12.4 KiB
Super App 2000
1570 

Length

Max length14
Median length14
Mean length14
Min length14

Characters and Unicode

Total characters21980
Distinct characters9
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowSuper App 2000
2nd rowSuper App 2000
3rd rowSuper App 2000
4th rowSuper App 2000
5th rowSuper App 2000

Common Values

ValueCountFrequency (%)
Super App 2000 1570
100.0%

Length

2024-07-01T02:37:44.239863image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-07-01T02:37:44.463279image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
ValueCountFrequency (%)
super 1570
33.3%
app 1570
33.3%
2000 1570
33.3%

Most occurring characters

ValueCountFrequency (%)
p 4710
21.4%
0 4710
21.4%
3140
14.3%
S 1570
 
7.1%
u 1570
 
7.1%
e 1570
 
7.1%
r 1570
 
7.1%
A 1570
 
7.1%
2 1570
 
7.1%

Most occurring categories

ValueCountFrequency (%)
(unknown) 21980
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
p 4710
21.4%
0 4710
21.4%
3140
14.3%
S 1570
 
7.1%
u 1570
 
7.1%
e 1570
 
7.1%
r 1570
 
7.1%
A 1570
 
7.1%
2 1570
 
7.1%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 21980
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
p 4710
21.4%
0 4710
21.4%
3140
14.3%
S 1570
 
7.1%
u 1570
 
7.1%
e 1570
 
7.1%
r 1570
 
7.1%
A 1570
 
7.1%
2 1570
 
7.1%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 21980
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
p 4710
21.4%
0 4710
21.4%
3140
14.3%
S 1570
 
7.1%
u 1570
 
7.1%
e 1570
 
7.1%
r 1570
 
7.1%
A 1570
 
7.1%
2 1570
 
7.1%

campaign_id
Categorical

IMBALANCE 

Distinct7
Distinct (%)0.4%
Missing0
Missing (%)0.0%
Memory size12.4 KiB
campaign_16
1373 
campaign_6
 
61
campaign_10
 
61
campaign_9
 
43
campaign_5
 
20
Other values (2)
 
12

Length

Max length11
Median length11
Mean length10.914013
Min length10

Characters and Unicode

Total characters17135
Distinct characters15
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1 ?
Unique (%)0.1%

Sample

1st rowcampaign_16
2nd rowcampaign_16
3rd rowcampaign_16
4th rowcampaign_16
5th rowcampaign_16

Common Values

ValueCountFrequency (%)
campaign_16 1373
87.5%
campaign_6 61
 
3.9%
campaign_10 61
 
3.9%
campaign_9 43
 
2.7%
campaign_5 20
 
1.3%
campaign_4 11
 
0.7%
campaign_13 1
 
0.1%

Length

2024-07-01T02:37:44.643300image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-07-01T02:37:44.855235image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
ValueCountFrequency (%)
campaign_16 1373
87.5%
campaign_6 61
 
3.9%
campaign_10 61
 
3.9%
campaign_9 43
 
2.7%
campaign_5 20
 
1.3%
campaign_4 11
 
0.7%
campaign_13 1
 
0.1%

Most occurring characters

ValueCountFrequency (%)
a 3140
18.3%
c 1570
9.2%
m 1570
9.2%
p 1570
9.2%
i 1570
9.2%
g 1570
9.2%
n 1570
9.2%
_ 1570
9.2%
1 1435
8.4%
6 1434
8.4%
Other values (5) 136
 
0.8%

Most occurring categories

ValueCountFrequency (%)
(unknown) 17135
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
a 3140
18.3%
c 1570
9.2%
m 1570
9.2%
p 1570
9.2%
i 1570
9.2%
g 1570
9.2%
n 1570
9.2%
_ 1570
9.2%
1 1435
8.4%
6 1434
8.4%
Other values (5) 136
 
0.8%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 17135
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
a 3140
18.3%
c 1570
9.2%
m 1570
9.2%
p 1570
9.2%
i 1570
9.2%
g 1570
9.2%
n 1570
9.2%
_ 1570
9.2%
1 1435
8.4%
6 1434
8.4%
Other values (5) 136
 
0.8%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 17135
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
a 3140
18.3%
c 1570
9.2%
m 1570
9.2%
p 1570
9.2%
i 1570
9.2%
g 1570
9.2%
n 1570
9.2%
_ 1570
9.2%
1 1435
8.4%
6 1434
8.4%
Other values (5) 136
 
0.8%

campaign_name
Categorical

IMBALANCE 

Distinct7
Distinct (%)0.4%
Missing0
Missing (%)0.0%
Memory size12.4 KiB
Super campaign 16
1373 
Super campaign 6
 
61
Super campaign 10
 
61
Super campaign 9
 
43
Super campaign 5
 
20
Other values (2)
 
12

Length

Max length17
Median length17
Mean length16.914013
Min length16

Characters and Unicode

Total characters26555
Distinct characters19
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1 ?
Unique (%)0.1%

Sample

1st rowSuper campaign 16
2nd rowSuper campaign 16
3rd rowSuper campaign 16
4th rowSuper campaign 16
5th rowSuper campaign 16

Common Values

ValueCountFrequency (%)
Super campaign 16 1373
87.5%
Super campaign 6 61
 
3.9%
Super campaign 10 61
 
3.9%
Super campaign 9 43
 
2.7%
Super campaign 5 20
 
1.3%
Super campaign 4 11
 
0.7%
Super campaign 13 1
 
0.1%

Length

2024-07-01T02:37:45.450195image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-07-01T02:37:45.775236image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
ValueCountFrequency (%)
super 1570
33.3%
campaign 1570
33.3%
16 1373
29.2%
6 61
 
1.3%
10 61
 
1.3%
9 43
 
0.9%
5 20
 
0.4%
4 11
 
0.2%
13 1
 
< 0.1%

Most occurring characters

ValueCountFrequency (%)
p 3140
11.8%
3140
11.8%
a 3140
11.8%
S 1570
 
5.9%
m 1570
 
5.9%
n 1570
 
5.9%
g 1570
 
5.9%
u 1570
 
5.9%
i 1570
 
5.9%
c 1570
 
5.9%
Other values (9) 6145
23.1%

Most occurring categories

ValueCountFrequency (%)
(unknown) 26555
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
p 3140
11.8%
3140
11.8%
a 3140
11.8%
S 1570
 
5.9%
m 1570
 
5.9%
n 1570
 
5.9%
g 1570
 
5.9%
u 1570
 
5.9%
i 1570
 
5.9%
c 1570
 
5.9%
Other values (9) 6145
23.1%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 26555
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
p 3140
11.8%
3140
11.8%
a 3140
11.8%
S 1570
 
5.9%
m 1570
 
5.9%
n 1570
 
5.9%
g 1570
 
5.9%
u 1570
 
5.9%
i 1570
 
5.9%
c 1570
 
5.9%
Other values (9) 6145
23.1%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 26555
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
p 3140
11.8%
3140
11.8%
a 3140
11.8%
S 1570
 
5.9%
m 1570
 
5.9%
n 1570
 
5.9%
g 1570
 
5.9%
u 1570
 
5.9%
i 1570
 
5.9%
c 1570
 
5.9%
Other values (9) 6145
23.1%

ad_id
Text

MISSING 

Distinct54
Distinct (%)3.7%
Missing117
Missing (%)7.5%
Memory size12.4 KiB
2024-07-01T02:37:46.463847image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Length

Max length10
Median length10
Mean length9.6703372
Min length4

Characters and Unicode

Total characters14051
Distinct characters63
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique2 ?
Unique (%)0.1%

Sample

1st rowad_16L8hGR
2nd rowad_16z49oF
3rd rowad_16DpJ5e
4th rowad_16sutOl
5th rowad_16ZEk4H
ValueCountFrequency (%)
ad_6 61
 
4.2%
ad_16n7joh 42
 
2.9%
ad_16sutol 39
 
2.7%
ad_1690b1k 35
 
2.4%
ad_160kffb 35
 
2.4%
ad_16tdi2h 35
 
2.4%
ad_16vaipg 35
 
2.4%
ad_1658mji 34
 
2.3%
ad_16iiljw 34
 
2.3%
ad_162vcyt 34
 
2.3%
Other values (44) 1069
73.6%
2024-07-01T02:37:47.402290image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
1 1560
 
11.1%
d 1546
 
11.0%
6 1535
 
10.9%
a 1511
 
10.8%
_ 1453
 
10.3%
R 230
 
1.6%
J 213
 
1.5%
k 205
 
1.5%
9 200
 
1.4%
T 196
 
1.4%
Other values (53) 5402
38.4%

Most occurring categories

ValueCountFrequency (%)
(unknown) 14051
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
1 1560
 
11.1%
d 1546
 
11.0%
6 1535
 
10.9%
a 1511
 
10.8%
_ 1453
 
10.3%
R 230
 
1.6%
J 213
 
1.5%
k 205
 
1.5%
9 200
 
1.4%
T 196
 
1.4%
Other values (53) 5402
38.4%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 14051
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
1 1560
 
11.1%
d 1546
 
11.0%
6 1535
 
10.9%
a 1511
 
10.8%
_ 1453
 
10.3%
R 230
 
1.6%
J 213
 
1.5%
k 205
 
1.5%
9 200
 
1.4%
T 196
 
1.4%
Other values (53) 5402
38.4%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 14051
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
1 1560
 
11.1%
d 1546
 
11.0%
6 1535
 
10.9%
a 1511
 
10.8%
_ 1453
 
10.3%
R 230
 
1.6%
J 213
 
1.5%
k 205
 
1.5%
9 200
 
1.4%
T 196
 
1.4%
Other values (53) 5402
38.4%

ad_name
Text

MISSING 

Distinct54
Distinct (%)3.7%
Missing117
Missing (%)7.5%
Memory size12.4 KiB
2024-07-01T02:37:47.843129image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Length

Max length16
Median length16
Mean length15.670337
Min length10

Characters and Unicode

Total characters22769
Distinct characters63
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique2 ?
Unique (%)0.1%

Sample

1st rowSuper AD 16L8hGR
2nd rowSuper AD 16z49oF
3rd rowSuper AD 16DpJ5e
4th rowSuper AD 16sutOl
5th rowSuper AD 16ZEk4H
ValueCountFrequency (%)
super 1453
33.3%
ad 1453
33.3%
6 61
 
1.4%
16n7joh 42
 
1.0%
16sutol 39
 
0.9%
16tdi2h 35
 
0.8%
16vaipg 35
 
0.8%
160kffb 35
 
0.8%
1690b1k 35
 
0.8%
1658mji 34
 
0.8%
Other values (46) 1137
26.1%
2024-07-01T02:37:48.883644image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
2906
12.8%
u 1641
 
7.2%
D 1599
 
7.0%
p 1581
 
6.9%
A 1578
 
6.9%
S 1561
 
6.9%
1 1560
 
6.9%
e 1547
 
6.8%
6 1535
 
6.7%
r 1529
 
6.7%
Other values (53) 5732
25.2%

Most occurring categories

ValueCountFrequency (%)
(unknown) 22769
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
2906
12.8%
u 1641
 
7.2%
D 1599
 
7.0%
p 1581
 
6.9%
A 1578
 
6.9%
S 1561
 
6.9%
1 1560
 
6.9%
e 1547
 
6.8%
6 1535
 
6.7%
r 1529
 
6.7%
Other values (53) 5732
25.2%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 22769
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
2906
12.8%
u 1641
 
7.2%
D 1599
 
7.0%
p 1581
 
6.9%
A 1578
 
6.9%
S 1561
 
6.9%
1 1560
 
6.9%
e 1547
 
6.8%
6 1535
 
6.7%
r 1529
 
6.7%
Other values (53) 5732
25.2%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 22769
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
2906
12.8%
u 1641
 
7.2%
D 1599
 
7.0%
p 1581
 
6.9%
A 1578
 
6.9%
S 1561
 
6.9%
1 1560
 
6.9%
e 1547
 
6.8%
6 1535
 
6.7%
r 1529
 
6.7%
Other values (53) 5732
25.2%

impressions
Real number (ℝ)

MISSING  ZEROS 

Distinct1050
Distinct (%)70.5%
Missing81
Missing (%)5.2%
Infinite0
Infinite (%)0.0%
Mean228917.58
Minimum0
Maximum5825656
Zeros165
Zeros (%)10.5%
Negative0
Negative (%)0.0%
Memory size12.4 KiB
2024-07-01T02:37:49.466371image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q148
median4984
Q374244
95-th percentile1611193.6
Maximum5825656
Range5825656
Interquartile range (IQR)74196

Descriptive statistics

Standard deviation622374.42
Coefficient of variation (CV)2.7187708
Kurtosis23.272587
Mean228917.58
Median Absolute Deviation (MAD)4984
Skewness4.2987887
Sum3.4085827 × 108
Variance3.8734992 × 1011
MonotonicityNot monotonic
2024-07-01T02:37:49.943793image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 165
 
10.5%
4 17
 
1.1%
10 16
 
1.0%
24 13
 
0.8%
2 12
 
0.8%
16 12
 
0.8%
12 12
 
0.8%
14 12
 
0.8%
38 11
 
0.7%
8 11
 
0.7%
Other values (1040) 1208
76.9%
(Missing) 81
 
5.2%
ValueCountFrequency (%)
0 165
10.5%
2 12
 
0.8%
4 17
 
1.1%
6 10
 
0.6%
8 11
 
0.7%
10 16
 
1.0%
12 12
 
0.8%
14 12
 
0.8%
16 12
 
0.8%
18 7
 
0.4%
ValueCountFrequency (%)
5825656 1
0.1%
5416918 1
0.1%
5242288 1
0.1%
5159238 1
0.1%
4998186 1
0.1%
4380112 1
0.1%
4268296 1
0.1%
3832006 1
0.1%
3808492 1
0.1%
3805026 1
0.1%

clicks
Real number (ℝ)

MISSING  SKEWED  ZEROS 

Distinct615
Distinct (%)41.3%
Missing81
Missing (%)5.2%
Infinite0
Infinite (%)0.0%
Mean5648.4661
Minimum0
Maximum1215918
Zeros255
Zeros (%)16.2%
Negative0
Negative (%)0.0%
Memory size12.4 KiB
2024-07-01T02:37:50.407411image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q16
median90
Q31288
95-th percentile20852.8
Maximum1215918
Range1215918
Interquartile range (IQR)1282

Descriptive statistics

Standard deviation40067.795
Coefficient of variation (CV)7.093571
Kurtosis604.75505
Mean5648.4661
Median Absolute Deviation (MAD)90
Skewness22.250401
Sum8410566
Variance1.6054282 × 109
MonotonicityNot monotonic
2024-07-01T02:37:50.728803image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 255
 
16.2%
2 75
 
4.8%
4 34
 
2.2%
6 31
 
2.0%
12 25
 
1.6%
10 24
 
1.5%
14 24
 
1.5%
41214 23
 
1.5%
8 18
 
1.1%
24 17
 
1.1%
Other values (605) 963
61.3%
(Missing) 81
 
5.2%
ValueCountFrequency (%)
0 255
16.2%
2 75
 
4.8%
4 34
 
2.2%
6 31
 
2.0%
8 18
 
1.1%
10 24
 
1.5%
12 25
 
1.6%
14 24
 
1.5%
16 15
 
1.0%
18 12
 
0.8%
ValueCountFrequency (%)
1215918 1
0.1%
590558 1
0.1%
474214 1
0.1%
274372 1
0.1%
249414 1
0.1%
169358 1
0.1%
141340 1
0.1%
131506 1
0.1%
114034 1
0.1%
108768 1
0.1%

installs
Real number (ℝ)

MISSING 

Distinct149
Distinct (%)18.0%
Missing744
Missing (%)47.4%
Infinite0
Infinite (%)0.0%
Mean954.6707
Minimum2
Maximum83240
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size12.4 KiB
2024-07-01T02:37:51.194261image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Quantile statistics

Minimum2
5-th percentile2
Q14
median12
Q342
95-th percentile566.5
Maximum83240
Range83238
Interquartile range (IQR)38

Descriptive statistics

Standard deviation5317.7026
Coefficient of variation (CV)5.5701956
Kurtosis91.950542
Mean954.6707
Median Absolute Deviation (MAD)10
Skewness8.3501999
Sum788558
Variance28277961
MonotonicityNot monotonic
2024-07-01T02:37:51.676874image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
2 168
 
10.7%
4 95
 
6.1%
6 69
 
4.4%
8 43
 
2.7%
10 37
 
2.4%
14 33
 
2.1%
12 30
 
1.9%
16 21
 
1.3%
18 21
 
1.3%
20 17
 
1.1%
Other values (139) 292
 
18.6%
(Missing) 744
47.4%
ValueCountFrequency (%)
2 168
10.7%
4 95
6.1%
6 69
4.4%
8 43
 
2.7%
10 37
 
2.4%
12 30
 
1.9%
14 33
 
2.1%
16 21
 
1.3%
18 21
 
1.3%
20 17
 
1.1%
ValueCountFrequency (%)
83240 1
0.1%
44898 1
0.1%
43250 1
0.1%
42274 1
0.1%
40174 1
0.1%
29572 1
0.1%
27708 1
0.1%
27534 1
0.1%
27388 1
0.1%
24546 1
0.1%

spend
Real number (ℝ)

MISSING  ZEROS 

Distinct1018
Distinct (%)68.4%
Missing81
Missing (%)5.2%
Infinite0
Infinite (%)0.0%
Mean51.666529
Minimum0
Maximum4803.045
Zeros140
Zeros (%)8.9%
Negative0
Negative (%)0.0%
Memory size12.4 KiB
2024-07-01T02:37:51.990707image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10.0117
median0.68965
Q39.28655
95-th percentile109.90044
Maximum4803.045
Range4803.045
Interquartile range (IQR)9.27485

Descriptive statistics

Standard deviation294.34344
Coefficient of variation (CV)5.6969851
Kurtosis113.32955
Mean51.666529
Median Absolute Deviation (MAD)0.68965
Skewness9.6432718
Sum76931.461
Variance86638.062
MonotonicityNot monotonic
2024-07-01T02:37:52.485941image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 140
 
8.9%
0.0013 25
 
1.6%
0.00065 24
 
1.5%
0.00195 24
 
1.5%
0.0065 19
 
1.2%
0.0026 18
 
1.1%
0.0039 18
 
1.1%
0.00715 16
 
1.0%
0.00455 15
 
1.0%
0.00585 15
 
1.0%
Other values (1008) 1175
74.8%
(Missing) 81
 
5.2%
ValueCountFrequency (%)
0 140
8.9%
0.00065 24
 
1.5%
0.0013 25
 
1.6%
0.00195 24
 
1.5%
0.0026 18
 
1.1%
0.00325 13
 
0.8%
0.0039 18
 
1.1%
0.00455 15
 
1.0%
0.0052 9
 
0.6%
0.00585 15
 
1.0%
ValueCountFrequency (%)
4803.045 1
0.1%
4279.925 1
0.1%
3392.415 1
0.1%
3284.125 1
0.1%
3228.355 1
0.1%
2097.745 1
0.1%
2081.82 1
0.1%
2004.405 1
0.1%
1836.12 1
0.1%
1825.07 1
0.1%

event_name
Categorical

CONSTANT 

Distinct1
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size12.4 KiB
super_event
1570 

Length

Max length11
Median length11
Mean length11
Min length11

Characters and Unicode

Total characters17270
Distinct characters9
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowsuper_event
2nd rowsuper_event
3rd rowsuper_event
4th rowsuper_event
5th rowsuper_event

Common Values

ValueCountFrequency (%)
super_event 1570
100.0%

Length

2024-07-01T02:37:52.840878image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-07-01T02:37:53.125460image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
ValueCountFrequency (%)
super_event 1570
100.0%

Most occurring characters

ValueCountFrequency (%)
e 4710
27.3%
s 1570
 
9.1%
u 1570
 
9.1%
p 1570
 
9.1%
r 1570
 
9.1%
_ 1570
 
9.1%
v 1570
 
9.1%
n 1570
 
9.1%
t 1570
 
9.1%

Most occurring categories

ValueCountFrequency (%)
(unknown) 17270
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
e 4710
27.3%
s 1570
 
9.1%
u 1570
 
9.1%
p 1570
 
9.1%
r 1570
 
9.1%
_ 1570
 
9.1%
v 1570
 
9.1%
n 1570
 
9.1%
t 1570
 
9.1%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 17270
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
e 4710
27.3%
s 1570
 
9.1%
u 1570
 
9.1%
p 1570
 
9.1%
r 1570
 
9.1%
_ 1570
 
9.1%
v 1570
 
9.1%
n 1570
 
9.1%
t 1570
 
9.1%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 17270
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
e 4710
27.3%
s 1570
 
9.1%
u 1570
 
9.1%
p 1570
 
9.1%
r 1570
 
9.1%
_ 1570
 
9.1%
v 1570
 
9.1%
n 1570
 
9.1%
t 1570
 
9.1%

events_d0
Real number (ℝ)

MISSING  ZEROS 

Distinct34
Distinct (%)4.1%
Missing744
Missing (%)47.4%
Infinite0
Infinite (%)0.0%
Mean4.3220339
Minimum0
Maximum82
Zeros511
Zeros (%)32.5%
Negative0
Negative (%)0.0%
Memory size12.4 KiB
2024-07-01T02:37:53.480416image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q32
95-th percentile26
Maximum82
Range82
Interquartile range (IQR)2

Descriptive statistics

Standard deviation10.845378
Coefficient of variation (CV)2.5093228
Kurtosis15.929108
Mean4.3220339
Median Absolute Deviation (MAD)0
Skewness3.7717377
Sum3570
Variance117.62223
MonotonicityNot monotonic
2024-07-01T02:37:53.938123image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram with fixed size bins (bins=34)
ValueCountFrequency (%)
0 511
32.5%
2 119
 
7.6%
4 59
 
3.8%
6 19
 
1.2%
8 19
 
1.2%
16 11
 
0.7%
12 9
 
0.6%
14 9
 
0.6%
26 9
 
0.6%
24 7
 
0.4%
Other values (24) 54
 
3.4%
(Missing) 744
47.4%
ValueCountFrequency (%)
0 511
32.5%
2 119
 
7.6%
4 59
 
3.8%
6 19
 
1.2%
8 19
 
1.2%
10 6
 
0.4%
12 9
 
0.6%
14 9
 
0.6%
16 11
 
0.7%
18 4
 
0.3%
ValueCountFrequency (%)
82 1
 
0.1%
78 1
 
0.1%
72 1
 
0.1%
64 2
0.1%
62 1
 
0.1%
60 1
 
0.1%
58 1
 
0.1%
56 1
 
0.1%
52 4
0.3%
50 2
0.1%

events_d7
Real number (ℝ)

MISSING  ZEROS 

Distinct41
Distinct (%)5.0%
Missing744
Missing (%)47.4%
Infinite0
Infinite (%)0.0%
Mean5.4915254
Minimum0
Maximum116
Zeros494
Zeros (%)31.5%
Negative0
Negative (%)0.0%
Memory size12.4 KiB
2024-07-01T02:37:54.465761image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q34
95-th percentile35.5
Maximum116
Range116
Interquartile range (IQR)4

Descriptive statistics

Standard deviation14.0542
Coefficient of variation (CV)2.5592524
Kurtosis16.815685
Mean5.4915254
Median Absolute Deviation (MAD)0
Skewness3.8621439
Sum4536
Variance197.52053
MonotonicityNot monotonic
2024-07-01T02:37:54.806872image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram with fixed size bins (bins=41)
ValueCountFrequency (%)
0 494
31.5%
2 113
 
7.2%
4 65
 
4.1%
6 25
 
1.6%
8 17
 
1.1%
10 13
 
0.8%
22 7
 
0.4%
14 7
 
0.4%
16 6
 
0.4%
18 6
 
0.4%
Other values (31) 73
 
4.6%
(Missing) 744
47.4%
ValueCountFrequency (%)
0 494
31.5%
2 113
 
7.2%
4 65
 
4.1%
6 25
 
1.6%
8 17
 
1.1%
10 13
 
0.8%
12 6
 
0.4%
14 7
 
0.4%
16 6
 
0.4%
18 6
 
0.4%
ValueCountFrequency (%)
116 1
 
0.1%
92 1
 
0.1%
90 2
0.1%
82 1
 
0.1%
80 1
 
0.1%
76 2
0.1%
72 3
0.2%
66 2
0.1%
64 1
 
0.1%
62 1
 
0.1%

unique_events_d0
Real number (ℝ)

MISSING  ZEROS 

Distinct29
Distinct (%)3.5%
Missing744
Missing (%)47.4%
Infinite0
Infinite (%)0.0%
Mean3.5738499
Minimum0
Maximum58
Zeros511
Zeros (%)32.5%
Negative0
Negative (%)0.0%
Memory size12.4 KiB
2024-07-01T02:37:55.120422image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q32
95-th percentile24
Maximum58
Range58
Interquartile range (IQR)2

Descriptive statistics

Standard deviation8.8462966
Coefficient of variation (CV)2.4752849
Kurtosis13.189366
Mean3.5738499
Median Absolute Deviation (MAD)0
Skewness3.5439918
Sum2952
Variance78.256964
MonotonicityNot monotonic
2024-07-01T02:37:55.553977image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram with fixed size bins (bins=29)
ValueCountFrequency (%)
0 511
32.5%
2 149
 
9.5%
4 47
 
3.0%
6 16
 
1.0%
8 14
 
0.9%
12 12
 
0.8%
24 8
 
0.5%
14 8
 
0.5%
10 7
 
0.4%
22 6
 
0.4%
Other values (19) 48
 
3.1%
(Missing) 744
47.4%
ValueCountFrequency (%)
0 511
32.5%
2 149
 
9.5%
4 47
 
3.0%
6 16
 
1.0%
8 14
 
0.9%
10 7
 
0.4%
12 12
 
0.8%
14 8
 
0.5%
16 5
 
0.3%
18 4
 
0.3%
ValueCountFrequency (%)
58 1
 
0.1%
56 1
 
0.1%
54 1
 
0.1%
52 1
 
0.1%
50 2
0.1%
48 1
 
0.1%
46 1
 
0.1%
44 3
0.2%
42 3
0.2%
40 4
0.3%

unique_events_d7
Real number (ℝ)

MISSING  ZEROS 

Distinct31
Distinct (%)3.8%
Missing744
Missing (%)47.4%
Infinite0
Infinite (%)0.0%
Mean4.2106538
Minimum0
Maximum68
Zeros494
Zeros (%)31.5%
Negative0
Negative (%)0.0%
Memory size12.4 KiB
2024-07-01T02:37:55.880944image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q32
95-th percentile29.5
Maximum68
Range68
Interquartile range (IQR)2

Descriptive statistics

Standard deviation10.448334
Coefficient of variation (CV)2.4814043
Kurtosis12.69885
Mean4.2106538
Median Absolute Deviation (MAD)0
Skewness3.5130522
Sum3478
Variance109.16769
MonotonicityNot monotonic
2024-07-01T02:37:56.207115image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram with fixed size bins (bins=31)
ValueCountFrequency (%)
0 494
31.5%
2 147
 
9.4%
4 58
 
3.7%
8 17
 
1.1%
6 15
 
1.0%
14 14
 
0.9%
10 9
 
0.6%
18 8
 
0.5%
32 6
 
0.4%
12 6
 
0.4%
Other values (21) 52
 
3.3%
(Missing) 744
47.4%
ValueCountFrequency (%)
0 494
31.5%
2 147
 
9.4%
4 58
 
3.7%
6 15
 
1.0%
8 17
 
1.1%
10 9
 
0.6%
12 6
 
0.4%
14 14
 
0.9%
18 8
 
0.5%
20 2
 
0.1%
ValueCountFrequency (%)
68 1
 
0.1%
62 2
 
0.1%
60 1
 
0.1%
58 1
 
0.1%
56 2
 
0.1%
54 1
 
0.1%
52 5
0.3%
50 2
 
0.1%
46 4
0.3%
44 2
 
0.1%

Interactions

2024-07-01T02:37:36.910917image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-07-01T02:37:19.921793image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-07-01T02:37:21.899560image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-07-01T02:37:24.672749image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-07-01T02:37:27.287727image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-07-01T02:37:29.729076image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-07-01T02:37:32.055936image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-07-01T02:37:34.656312image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-07-01T02:37:37.215283image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-07-01T02:37:20.122972image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-07-01T02:37:22.289344image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-07-01T02:37:25.020145image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-07-01T02:37:27.646839image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-07-01T02:37:30.083958image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-07-01T02:37:32.382027image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-07-01T02:37:34.826596image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-07-01T02:37:37.567220image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-07-01T02:37:20.308820image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-07-01T02:37:22.657194image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-07-01T02:37:25.367848image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-07-01T02:37:27.934649image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-07-01T02:37:30.431055image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-07-01T02:37:32.744903image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-07-01T02:37:35.138880image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-07-01T02:37:37.873313image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-07-01T02:37:20.475534image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-07-01T02:37:22.940807image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-07-01T02:37:25.657571image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-07-01T02:37:28.204199image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-07-01T02:37:30.734807image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-07-01T02:37:33.089129image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-07-01T02:37:35.439178image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-07-01T02:37:38.164334image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-07-01T02:37:20.624679image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-07-01T02:37:23.258087image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-07-01T02:37:25.984258image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-07-01T02:37:28.453350image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-07-01T02:37:30.999044image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-07-01T02:37:33.577676image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-07-01T02:37:35.728118image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-07-01T02:37:38.489715image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-07-01T02:37:20.893922image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-07-01T02:37:23.589874image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-07-01T02:37:26.300210image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-07-01T02:37:28.785065image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-07-01T02:37:31.143087image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-07-01T02:37:33.811822image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-07-01T02:37:36.005184image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-07-01T02:37:38.859110image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-07-01T02:37:21.234157image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-07-01T02:37:23.937172image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-07-01T02:37:26.630707image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-07-01T02:37:29.095131image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-07-01T02:37:31.395979image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-07-01T02:37:34.127442image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-07-01T02:37:36.289561image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-07-01T02:37:39.234477image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-07-01T02:37:21.553144image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-07-01T02:37:24.288930image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-07-01T02:37:27.009388image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-07-01T02:37:29.382822image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-07-01T02:37:31.726170image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-07-01T02:37:34.414412image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-07-01T02:37:36.599905image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Missing values

2024-07-01T02:37:39.744690image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
A simple visualization of nullity by column.
2024-07-01T02:37:40.591034image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.
2024-07-01T02:37:41.479862image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
The correlation heatmap measures nullity correlation: how strongly the presence or absence of one variable affects the presence of another.

Sample

summary_dateapp_idapp_typeapp_namecampaign_idcampaign_namead_idad_nameimpressionsclicksinstallsspendevent_nameevents_d0events_d7unique_events_d0unique_events_d7
02022.06.14com.super.appandroidSuper App 2000campaign_16Super campaign 16ad_16L8hGRSuper AD 16L8hGR82.046.0NaN0.00455super_eventNaNNaNNaNNaN
12022.07.10com.super.appandroidSuper App 2000campaign_16Super campaign 16ad_16z49oFSuper AD 16z49oF1942.01226.02.00.88920super_event0.00.00.00.0
22022.07.05com.super.appandroidSuper App 2000campaign_16Super campaign 16ad_16DpJ5eSuper AD 16DpJ5e116.02.0NaN0.00715super_eventNaNNaNNaNNaN
32022.06.01com.super.appandroidSuper App 2000campaign_16Super campaign 16ad_16sutOlSuper AD 16sutOl15550.018.04.00.52455super_event0.00.00.00.0
42022.07.25com.super.appandroidSuper App 2000campaign_16Super campaign 16ad_16ZEk4HSuper AD 16ZEk4H0.00.02.00.00000super_event0.00.00.00.0
52022.06.29com.super.appandroidSuper App 2000campaign_16Super campaign 16ad_16zXhnZSuper AD 16zXhnZ466.0264.02.00.24765super_event0.00.00.00.0
62022.06.02com.super.appandroidSuper App 2000campaign_16Super campaign 16ad_167wQ9KSuper AD 167wQ9K12.00.0NaN0.00130super_eventNaNNaNNaNNaN
72022.07.02com.super.appandroidSuper App 2000campaign_16Super campaign 16ad_165Ir4sSuper AD 165Ir4s0.00.0NaN0.00000super_eventNaNNaNNaNNaN
82022.06.02com.super.appandroidSuper App 2000campaign_16Super campaign 16ad_16SmxpJSuper AD 16SmxpJ10.00.0NaN0.00065super_eventNaNNaNNaNNaN
92022.07.27com.super.appandroidSuper App 2000campaign_16Super campaign 16ad_16OvRBMSuper AD 16OvRBM7382.0614.0NaN2.21520super_eventNaNNaNNaNNaN
summary_dateapp_idapp_typeapp_namecampaign_idcampaign_namead_idad_nameimpressionsclicksinstallsspendevent_nameevents_d0events_d7unique_events_d0unique_events_d7
15602022.06.04com.super.appandroidSuper App 2000campaign_16Super campaign 16ad_165Ir4sSuper AD 165Ir4s22.02.0NaN0.00260super_eventNaNNaNNaNNaN
15612022.07.06com.super.appandroidSuper App 2000campaign_16Super campaign 16ad_16aRyUOSuper AD 16aRyUO10.04.0NaN0.00000super_eventNaNNaNNaNNaN
15622022.07.09com.super.appandroidSuper App 2000campaign_16Super campaign 16ad_16V95hgSuper AD 16V95hg19304.012.0NaN0.31395super_eventNaNNaNNaNNaN
15632022.06.02com.super.appandroidSuper App 2000campaign_16Super campaign 16ad_16iTkkRSuper AD 16iTkkR76552.092.018.04.31925super_event0.00.00.00.0
15642022.06.16com.super.appandroidSuper App 2000campaign_16Super campaign 16ad_16L8hGRSuper AD 16L8hGR130.052.0NaN0.00390super_eventNaNNaNNaNNaN
15652022.06.25com.super.appandroidSuper App 2000campaign_16Super campaign 16ad_16tke86Super AD 16tke8662954.0162.0NaN11.77605super_eventNaNNaNNaNNaN
15662022.06.11com.super.appandroidSuper App 2000campaign_16Super campaign 16ad_16Qnvp6Super AD 16Qnvp628.06.0NaN0.00455super_eventNaNNaNNaNNaN
15672022.07.09com.super.appandroidSuper App 2000campaign_16Super campaign 16ad_1658MjISuper AD 1658MjI868.0470.0NaN0.41340super_eventNaNNaNNaNNaN
15682022.07.08com.super.appandroidSuper App 2000campaign_16Super campaign 16ad_16VAIpgSuper AD 16VAIpg7376.04366.04.03.01665super_event0.00.00.00.0
15692022.07.02com.super.appandroidSuper App 2000campaign_16Super campaign 16ad_16L8hGRSuper AD 16L8hGR0.00.02.00.00000super_event0.00.00.00.0